ACARec attends over artist catalogs to generate CF embeddings for new tracks, more than doubling recall and NDCG versus content-only baselines in music recommendation.
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Casting top-K retrieval as an MDP over implicit-ALS posteriors with closed-form fold-in transitions, the paper reports that dynamics-aware planning (especially one-step lookahead) outperforms static top-K on multiple datasets under leave-last-n splits when using cosine similarity.
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Leveraging Artist Catalogs for Cold-Start Music Recommendation
ACARec attends over artist catalogs to generate CF embeddings for new tracks, more than doubling recall and NDCG versus content-only baselines in music recommendation.
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Planning over Matrix-Factorization MDPs for Candidate Generation
Casting top-K retrieval as an MDP over implicit-ALS posteriors with closed-form fold-in transitions, the paper reports that dynamics-aware planning (especially one-step lookahead) outperforms static top-K on multiple datasets under leave-last-n splits when using cosine similarity.